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高水平疲劳性收缩期间肌电图幅度变化的分析与模拟

Analysis and simulation of changes in EMG amplitude during high-level fatiguing contractions.

作者信息

Lowery Madeleine M, O'Malley Mark J

机构信息

Rehabilitation Institute of Chicago, 345 E. Superior St, Chicago, Illinois 60611, USA.

出版信息

IEEE Trans Biomed Eng. 2003 Sep;50(9):1052-62. doi: 10.1109/TBME.2003.816078.

Abstract

Changes in surface electromyographic (EMG) amplitude during sustained, fatiguing contractions are commonly attributed to variations in muscle fiber conduction velocity (MFCV), motor unit firing rates, transmembrane action potentials and the synchronization or recruitment of motor units. However, the relative contribution of each factor remains unclear. Analytical relationships relating changes in MFCV and mean motor unit firing rates to the root mean square (RMS) and average rectified (AR) value of the surface EMG signal are derived. The relationships are then confirmed using model simulation. The simulations and analysis illustrate the different behaviors of the surface EMG RMS and AR value with changing MFCV and firing rate, as the level of motor unit superposition varies. Levels of firing rate modulation and short-term synchronization that, combined with variations in MFCV, could cause changes in EMG amplitude similar to those observed during sustained isometric contraction of the brachioradialis at 80% of maximum voluntary contraction were estimated. While it is not possible to draw conclusions about changes in neural control without further information about the underlying motor unit activation patterns, the examples presented illustrate how a combined analytical and simulation approach may provide insight into the manner in which different factors affect EMG amplitude during sustained isometric contractions.

摘要

在持续的疲劳性收缩过程中,表面肌电图(EMG)振幅的变化通常归因于肌纤维传导速度(MFCV)、运动单位放电频率、跨膜动作电位以及运动单位的同步化或募集的变化。然而,每个因素的相对贡献仍不清楚。推导了MFCV变化和平均运动单位放电频率与表面EMG信号的均方根(RMS)和平均整流(AR)值之间的分析关系。然后使用模型模拟对这些关系进行了验证。模拟和分析表明,随着运动单位叠加水平的变化,表面EMG RMS和AR值随MFCV和放电频率变化呈现出不同的行为。估计了放电频率调制水平和短期同步化水平,这些与MFCV的变化相结合,可能导致EMG振幅的变化,类似于在肱桡肌以最大自主收缩的80%进行持续等长收缩期间观察到的变化。虽然在没有关于潜在运动单位激活模式的进一步信息的情况下,无法得出关于神经控制变化的结论,但所举的例子说明了综合分析和模拟方法如何能够深入了解不同因素在持续等长收缩期间影响EMG振幅的方式。

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